Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such...
To mitigate future educational challenges, the early childhood period is critical for cognitive development, so understanding the factors influencing child learning abilities is essential. This study investigates the impact of parenting techniques, s...
Federated learning (FL) is known to perform machine learning tasks in a distributed manner. Over the years, this has become an emerging technology, especially with various data protection and privacy policies being imposed. FL allows for performing m...
Wearable sensor technology is increasingly being integrated into educational settings, offering innovative approaches to enhance teaching and learning experiences. These devices track various physiological and environmental variables, providing valua...
With the rising global demand for aquatic products, aquaculture has become a cornerstone of food security and sustainability. This review comprehensively analyzes the application of deep learning in sustainable aquaculture, covering key areas such as...
The application of Machine Learning in Mineral Processing and Extractive Metallurgy has important benefits in terms of increasing the predictability and controllability of the processes, optimizing their performance, and improving maintenance. Howeve...
Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavio...
The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the vari...
Two technologies of great interest in recent years—Artificial Intelligence (AI) and massive wireless communication networks—have found a significant point of convergence through Federated Learning (FL). Federated Learning is a Machine Lea...
The large number of new services and applications and, in general, all our everyday activities resolve in data mass production: all these data can become a golden source of information that might be used to improve our lives, wellness and working day...
Project-based learning has been explored in a variety of contexts and different phases of education. Several implementation challenges are associated with project-based learning. Among these challenges is ensuring collaboration between students enrol...
The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge network with heterogeneous devices having different constraints can affect i...
Augmented reality (AR) has received increasing attention in the research literature as a fundamental pedagogical tool that can enhance learning at most educational levels. In academic contexts, this technology permits the superimposing of three-dimen...
The fourth industrial revolution has triggered a notable shift in engineering education, bringing the need to create new professionals. In this context, the active learning approach appears to be more important than ever. Nevertheless, to date quite...
Reinforcement learning (RL) allows an agent to solve sequential decision-making problems by interacting with an environment in a trial-and-error fashion. When these environments are very complex, pure random exploration of possible solutions often fa...
Community engaged learning (CEL) is a teaching methodology which aims to bridge the gap between academia and society by collaborating on community-based projects. Inspired by theories of experiential learning and social constructivism, CEL celebrates...
The internet of things (IoT) represents a disruptive concept that has been changing society in several ways. There have been several successful applications of IoT in the industry. For example, in transportation systems, the novel internet of vehicle...
Over the past decade, educators have utilized flipped learning to augment students’ learning outside of the classroom. The COVID-19 pandemic disruptions in regular classroom teaching and learning activities intensified the use of the approach....
Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations...
This entry examines the critical issue of misinformation within online learning environments following the COVID-19 pandemic, focusing on its types, spread, and consequences. It identifies key drivers of misinformation, such as reliance on unverified...
The integration of technology and new tools in engineering education has created opportunities for the advancement of geotechnical engineering education (GEE). Technology-enhanced learning (TEL) has been implemented in GEE by many educators, and its...
Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of o...
Pedestrian trajectory prediction is widely used in various applications, such as intelligent transportation systems, autonomous driving, and social robotics. Precisely forecasting surrounding pedestrians’ future trajectories can assist intellig...
Deep learning has aided in the improvement of diagnosis identification, evaluation, and the interpretation of muscle ultrasound images, which may benefit clinical personnel. Muscle ultrasound images presents challenges such as low image quality due t...
Deep learning has achieved remarkable progress, particularly in neuroimaging analysis. Deep learning applications have also been extended from adult to pediatric medical images, and thus, this paper aims to present a systematic review of this recent...
Reinforcement learning (RL) is being used more in medical imaging for segmentation, detection, registration, and classification. This survey provides a comprehensive overview of RL techniques applied in this domain, categorizing the literature based...
Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened nu...
Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from the emergence of expert systems in the 1960s to the wide popularity of deep learning today. In particular, inexpensive computing and storage infrastructu...
Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are para...
Following the COVID-19 outbreak, teaching and learning have been forced to move fully to the Internet rather than the conventional offline medium. As a result, the use of M-learning has risen dramatically, which was neither expected or anticipated. T...
This study explores how artificial intelligence (AI) will evolve and impact interactive learning models in the next two decades. Using a PRISMA-based Systematic Literature Review (SLR) approach, the study analyzes articles published in the last five...
(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause...
In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descenda...
With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerfu...
Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has exp...
Hebrew and Arabic are Semitic languages that use abjad alphabets, a consonant-primary writing system in which vowels are featured as optional diacritics. The relatively predictable morphology of Semitic language renders abjad writing feasible, with l...
In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field...
The sudden switch to emergency remote learning during the COVID-19 pandemic posed many challenges for learners, but it also provided the opportunity to research these challenges. This study empirically examines the relationships of the contextual cha...
Federated Learning (FL) offers a promising way to train machine learning models collaboratively on decentralized edge devices, addressing key privacy, communication, and regulatory challenges in smart city environments. This survey adopts a narrative...
This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demo...
Background/Objectives: Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enabling predictive, diagnostic, and therapeutic advancements. Pediatric healthcare presents unique challenges, including limited data availa...
Federated learning is a distributed learning method used to solve data silos and privacy protection in machine learning, aiming to train global models together via multiple clients without sharing data. However, federated learning itself introduces c...
An increasing body of work provides evidence of the importance of bodily experience for cognition and the learning of mathematics. Sensor-based technologies have potential for guiding sensori-motor engagement with challenging mathematical ideas in ne...
Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every f...
The paper addresses the shift in architectural education regarding the need to develop new approaches in teaching methodology, improve curricula, and make advancements in new learning arenas and digital environments. The research is based on the assu...
Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Clou...
Recent advancements in data collection technologies, data science, and speech processing have fueled significant interest in the computational analysis of biological sounds. This enhanced analytical capability shows promise for improved understanding...
The Internet of Things (IoT) has established an exceptional ecosystem of interconnected devices where a vast multitude of heterogeneous devices can communicate, collect, and share data for enhanced decision-making processes. To effectively analyze th...
In recent years, novel technologies in smart healthcare systems have opened significant opportunities for diagnosis and treatment across various medical fields. Federated Learning (FL), a decentralized machine learning approach, trains shared models...
Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the superiorities of deep learning in feature representation with the merits of transfer learning in knowledge transference. This synergistic integration propels DTL...
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